Department Systems Analysis, Integrated Assessment and Modelling

Scientific Computing for Aquatic Research

High Performance Scientific Computing complements research originally grounded exclusively on experimental investigations and analytical thinking. The power of efficient computational algorithms deployed on parallel high performance compute clusters enables exploration of complex phenomena at a virtually unrestricted space and time scales. In particular, computational models, calibrated to available observation data, allow robust predictions of future evolution of probable scenarios, supported by reliable quantification of the associated uncertainties and risks.

Focus areas of the group are the following:

  • Parallel uncertainty quantification and propagation methodologies and numerical algorithms for deterministic and stochastic computational models
  • Multi-level and multi-fidelity methods for optimal complexity in statistical sampling algorithms
  • Massively parallel high performance computing, parallelization techniques and load balancing
  • Numerical methods for hyperbolic nonlinear partial differential equations (shallow water, Euler, multi-phase)


Dr. Jonas SukysHead Scientific ComputingTel. +41 58 765 5310Send Mail
Dr. Marco BacciTel. +41 58 765 6427Send Mail
Dr. Artur SafinPostdoctoral researcherTel. +41 58 765 6683Send Mail


Dr. Jonas SukysHead Scientific ComputingTel. +41 58 765 5310Send Mail


Heterogeneous data platform for operational modeling and forecasting of Swiss lakes in collaboration with the Swiss Data Science Center.
Scalable Bayesian inference framework for uncertainty quantification in stochastic models using thousands of processors in parallel at the Swiss Supercomputing Center and ETH Zurich.